631 citations · 2.1k across the 14 of their papers we have counts for
14 papers
Self-supervised Cross-silo Federated Neural Architecture Search
Xinle Liang, Yang Liu, Jiahuan Luo +3
Federated Learning (FL) provides both model performance and data privacy for machine learning tasks where samples or features are distributed among different parties. In the traini…
Backdoor attacks and defenses in feature-partitioned collaborative learning
Yang Liu, Zhihao Yi, Tianjian Chen
Since there are multiple parties in collaborative learning, malicious parties might manipulate the learning process for their own purposes through backdoor attacks. However, most o…
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
Ce Ju, Ruihui Zhao, Jichao Sun +11
Prevention of stroke with its associated risk factors has been one of the public health priorities worldwide. Emerging artificial intelligence technology is being increasingly adop…
Learning to Detect Malicious Clients for Robust Federated Learning
Suyi Li, Yong Cheng, Wei Wang +2
Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may init…
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Yang Liu, Anbu Huang, Yun Luo +7
Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to priv…
RPN: A Residual Pooling Network for Efficient Federated Learning
Anbu Huang, Yuanyuan Chen, Yang Liu +2
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security. Due to m…
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Kai Yang, Tao Fan, Tianjian Chen +2
Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers l…
A Communication Efficient Collaborative Learning Framework for Distributed Features
Yang Liu, Yan Kang, Xinwei Zhang +5
We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw…
Federated Transfer Reinforcement Learning for Autonomous Driving
Xinle Liang, Yang Liu, Tianjian Chen +2
Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uplo…
Abnormal Client Behavior Detection in Federated Learning
Suyi Li, Yong Cheng, Yang Liu +2
In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally devi…
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
Dashan Gao, Ce Ju, Xiguang Wei +3
Electroencephalography (EEG) classification techniques have been widely studied for human behavior and emotion recognition tasks. But it is still a challenging issue since the data…
Federated Machine Learning: Concept and Applications
Qiang Yang, Yang Liu, Tianjian Chen +1
Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and securit…
SecureBoost: A Lossless Federated Learning Framework
Kewei Cheng, Tao Fan, Yilun Jin +4
The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (…
Secure Federated Transfer Learning
Yang Liu, Yan Kang, Chaoping Xing +2
Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily…